Application of Machine Learning During Maintenance and Exploitation of Electric Vehicles

In the era of increasing demand for sources of renewable and cleaner energy, electric vehicles offer possible solutions in order to maintain and improve the mobility of transport systems. In parallel, the application of machine learning for digital twin technology greatly contributes to the development and optimization of vehicles and systems, saving time and resources, as well as material resources. In terms of electric vehicle components, electric batteries represent the most expensive elements where machine learning can help to optimize characteristics during exploitation and to predict maintenance time and their lifetime. This article related to the possibilities of future research, which, by intensifying the digitalization and machine learning for digital twin technology, will affect the improvement of the application and disposal of components, but the complete system of electric vehicles, during the entire life cycle, including the recycling.